add num_timesteps
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@ -1,2 +1,3 @@
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================ Training Loss (Sun Feb 23 15:46:44 2025) ================
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================ Training Loss (Sun Feb 23 15:46:44 2025) ================
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================ Training Loss (Sun Feb 23 15:52:29 2025) ================
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================ Training Loss (Sun Feb 23 15:52:29 2025) ================
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================ Training Loss (Sun Feb 23 16:00:07 2025) ================
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@ -76,6 +76,7 @@ nce_includes_all_negatives_from_minibatch: False
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serial_batches: False
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serial_batches: False
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stylegan2_G_num_downsampling: 1
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stylegan2_G_num_downsampling: 1
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suffix:
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suffix:
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tau: 0.1
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update_html_freq: 1000
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update_html_freq: 1000
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use_idt: False
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use_idt: False
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verbose: False
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verbose: False
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@ -222,6 +222,7 @@ class RomaUnsbModel(BaseModel):
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parser.add_argument('--atten_layers', type=str, default='1,3,5', help='compute Cross-Similarity on which layers')
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parser.add_argument('--atten_layers', type=str, default='1,3,5', help='compute Cross-Similarity on which layers')
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parser.add_argument('--tau', type=float, default=0.1, help='used in unsb')
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parser.add_argument('--tau', type=float, default=0.1, help='used in unsb')
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parser.add_argument('--num_timesteps', type=int, default=10, help='used in unsb')
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parser.set_defaults(pool_size=0) # no image pooling
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parser.set_defaults(pool_size=0) # no image pooling
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@ -28,4 +28,5 @@ python train.py \
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--num_patches 256 \
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--num_patches 256 \
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--flip_equivariance False \
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--flip_equivariance False \
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--eta_ratio 0.1 \
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--eta_ratio 0.1 \
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--tau 0.1
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--tau 0.1 \
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--num_timesteps 10
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